Deep Workpiece Region Segmentation for Bin Picking

@article{Khalid2019DeepWR,
  title={Deep Workpiece Region Segmentation for Bin Picking},
  author={Muhammad Usman Khalid and Janik M. Hager and Werner Kraus and Marco F. Huber and Marc Toussaint},
  journal={2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)},
  year={2019},
  pages={1138-1144}
}
For most industrial bin picking solutions, the pose of a workpiece is localized by matching a CAD model to point cloud obtained from 3D sensor. Distinguishing flat workpieces from bottom of the bin in point cloud imposes challenges in the localization of workpieces that lead to wrong or phantom detections. In this paper, we propose a framework that solves this problem by automatically segmenting workpiece regions from non-workpiece regions in a point cloud data. It is done in real time by… Expand
Multiple Object Detection of Workpieces Based on Fusion of Deep Learning and Image Processing*
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A workpiece detection method based on fusion of deep learning and image processing effectively corrects the bounding boxes obtained by deep learning, and obtains workpiece contour and gripping point information. Expand
One-Shot Shape-Based Amodal-to-Modal Instance Segmentation
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OSSIS is proposed, a single-stage One-Shot Shape-based Instance Segmentation algorithm that produces the target object modal segmentation mask in a depth image of a scene based only on a binary shape mask of thetarget object. Expand

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